I have been trying regression with scikit-learn with a problem with multiple outputs like this:
X = np.random.random((10,3)) y = np.random.random((10,2)) X2 = np.random.random((7,3)) clf = SVR() clf.fit(X, y) y_pred = clf.predict(X2)
The problem is that this doesn't work. It fails with:
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
Does anyone know how to deal with regression with multiple outputs in scikit-learn?
Edit. I have noticed RandomForestRegressor, KNeighborsRegressor, and LinearRegression all work, but rf is the only one that's close to being good on my dataset! Is there some way to fit